From 61453a707f79e39a5081c8fdb9a10ab9b44ee83d Mon Sep 17 00:00:00 2001 From: Ruben Date: Sat, 11 Mar 2017 11:49:47 +0100 Subject: [PATCH] Load from St.Jan --- charts.py | 6 +- colour.py | 109 +- images.db | Bin 458752 -> 778240 bytes images/1-0.png | Bin 0 -> 19769 bytes images/1-1.png | Bin 0 -> 5507 bytes images/1-10.png | Bin 0 -> 320 bytes images/1-11.png | Bin 0 -> 853 bytes images/1-2.png | Bin 0 -> 4575 bytes images/1-3.png | Bin 0 -> 5234 bytes images/1-4.png | Bin 0 -> 1092 bytes images/1-5.png | Bin 0 -> 1842 bytes images/1-6.png | Bin 0 -> 1347 bytes images/1-7.png | Bin 0 -> 3377 bytes images/1-8.png | Bin 0 -> 917 bytes images/1-9.png | Bin 0 -> 2565 bytes images/10-0.png | Bin 0 -> 18236 bytes images/10-1.png | Bin 0 -> 7644 bytes images/10-10.png | Bin 0 -> 457 bytes images/10-11.png | Bin 0 -> 363 bytes images/10-12.png | Bin 0 -> 366 bytes images/10-13.png | Bin 0 -> 345 bytes images/10-14.png | Bin 0 -> 294 bytes images/10-15.png | Bin 0 -> 369 bytes images/10-16.png 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a/charts.py +++ b/charts.py @@ -5,7 +5,8 @@ from lxml import etree def createPie(data, gaps = 0): total = sum(data.values()) colours = ['#268ED7','#6A4F8D', '#A03D4F','#DC7432','#F6DC3B','#76B33B', '#267B60'] - svg = '' + className = "hoverTitles" if len(data) > 9 else "" + svg = '' % className numbers = "" position = 90 i = 0 @@ -18,8 +19,9 @@ def createPie(data, gaps = 0): d = describeArc(float(diff['x']), float(diff['y']), 60, position, position+arc) # print(position, arc) svg += '' % (colours[i%len(colours)], d) + # tRadius = 105 if i%2 else 95 t = polarToCartesian(0,0, 100, position+0.5*arc) - numbers += '%s' % (t['x'], t['y'], position+0.5*arc, t['x'],t['y'], e) + svg += '%s' % (t['x'], t['y'], position+0.5*arc, t['x'],t['y'], e) position += arc i+=1 diff --git a/colour.py b/colour.py index 09dbf80..8479f12 100644 --- a/colour.py +++ b/colour.py @@ -7,13 +7,15 @@ import codecs import colorsys import math import numpy as np +import sklearn.cluster +from PIL import Image NUM_CLUSTERS = 64 def getColourAsHex(colour): return '#' + ''.join(format(c, '02x') for c in colour.astype(int)) -def getColoursForImageByClusters(image): +def getColoursForImageByKMeansClusters(image): """ Adapted on answers by Peter Hansen (http://stackoverflow.com/a/3244061) @@ -43,6 +45,62 @@ def getColoursForImageByClusters(image): # print(colours) return list(zip(codes, percentages)) +def getColoursForImageByMeanShiftClusters(image, saveImgName = False): + """ + Adapted on answers by + Peter Hansen (http://stackoverflow.com/a/3244061) + & Johan Mickos (http://stackoverflow.com/a/34140327) + Now using MeanShift clustering + + if saveImgName set to the path/prefix_ for the layer images, save images of each cluster matched pixels + """ + imgSize = (170,170) + # imgSize = (int(targetWidth), int(image.size[1] / (image.size[0]/targetWidth))) + im = image.copy() # optional, to reduce time + im.thumbnail(imgSize) + imgSize = im.size + print("\tRender image of", image.size,"for", imgSize) + imgAr = scipy.misc.fromimage(im) + shape = imgAr.shape + ar = imgAr.reshape(scipy.product(shape[:2]), shape[2]) + total = len(ar) + +# print( 'finding clusters') +# codes, dist = scipy.cluster.vq.kmeans(ar.astype(float), NUM_CLUSTERS) + bandwidth = sklearn.cluster.estimate_bandwidth(ar.astype(float), quantile=0.1, n_samples=500) + ms = sklearn.cluster.MeanShift(bandwidth=bandwidth) + ms.fit(ar.astype(float)) + labels = ms.labels_ # labels per point + cluster_centers = ms.cluster_centers_ # centers of found clusters + + labels_unique = np.unique(labels) + n_clusters_ = len(labels_unique) + + print("\tClusters found:", n_clusters_) + + colours = [] + for k in labels_unique: + cluster_center = cluster_centers[k] # np.array with rgb + mask = labels == k # True/False map of flattened array + # m = np.ma.masked_where(labels != k, labels) + count = len(ar[mask]) # nr of pixels for this label + + # pass on percentages: + colours.append((cluster_center, 100 * count / total)) + + if saveImgName: + # save image as RGBA with transparent pixels, except for there where label == k + layer = np.array([np.append(ar[i],255) if l == k else [0,0,0,0] for i,l in enumerate(list(labels))], dtype=np.uint8) + layerImgAr = layer.reshape((imgSize[1],imgSize[0],4)) # layers are stacked: vertical, horizontal, rgba + layerImg = Image.fromarray(layerImgAr, mode="RGBA") + layerFilename = saveImgName + '-%s.png' % k + layerImg.save(layerFilename) + print(layerFilename) + + # display(HTML("%s" % (getColourAsHex(cluster_center),getColourAsHex(cluster_center)))) + + return colours + def getColoursForImageByPxAvg(image): im = image.copy().resize((8, 8)) pixels = np.concatenate(scipy.misc.fromimage(im)) @@ -54,36 +112,41 @@ def getColoursAsHTML(colours): return " ".join(['%s - (%s %%)' % (getColourAsHex(colour[0]), getColourAsHex(colour[0]), colour[1]) for colour in colours]); def loadColoursFromDbImages(images): - return [i.colours for i in images] + return [i.colours for i in images if not i.colours is None] def getSvgFromDbImages(images, elId = ""): # sum concatenates all colour arrays - allColours = [] - for c in loadColoursFromDbImages(images): - allColours += c + # allColours = [] + # for c in loadColoursFromDbImages(images): + # allColours += c # box 160, because center or circle = 100 => +/- 50 => + r of colour circle (max: 10) => 160 - svg = '' % elId + svg = '' % (elId, ) radius = 100 - for colour in allColours: - rgb, percentage = colour - rgbNorm = rgb/255 - hsv = colorsys.rgb_to_hsv(rgbNorm[0], rgbNorm[1], rgbNorm[2]) - # find position on circle - radians = 2 * math.pi * hsv[0] - x = math.cos(radians) - y = math.sin(radians) - - # based on saturation, we move inwards/outwards - # min = 0.5, max = 1.5 (dus + 0.5) - pos = np.array([x,y]) * (0.5 + hsv[1]) * radius - # Posibilitiy: determine position based on avg(saturation, value) => dark & grey inside, shiney and colourful outside - # pos = np.array([x,y]) * (0.5 + (hsv[1]+hsv[2])/2) * radius - r = max(1,-10/percentage+10) # as r, we converge to maximum radius 10, but don't want to get smaller radi then 1 - c = '' % (pos[0], pos[1], r, getColourAsHex(rgb)) - svg += c + for image in images: + if image.colours is None: + continue + + for i, colour in enumerate(image.colours): + colourId = '%s-%s' % (image.id, i) + rgb, percentage = colour + rgbNorm = rgb/255 + hsv = colorsys.rgb_to_hsv(rgbNorm[0], rgbNorm[1], rgbNorm[2]) + # find position on circle + radians = 2 * math.pi * hsv[0] + x = math.cos(radians) + y = math.sin(radians) + + # based on saturation, we move inwards/outwards + # min = 0.5, max = 1.5 (dus + 0.5) + pos = np.array([x,y]) * (0.5 + hsv[1]) * radius + # Posibilitiy: determine position based on avg(saturation, value) => dark & grey inside, shiney and colourful outside + # pos = np.array([x,y]) * (0.5 + (hsv[1]+hsv[2])/2) * radius + r = max(1,-10/percentage+10) # as r, we converge to maximum radius 10, but don't want to get smaller radi then 1 + c = '' % (pos[0], 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diff --git a/images/117-11.png b/images/117-11.png
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diff --git a/images/117-13.png b/images/117-13.png
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diff --git a/images/117-2.png b/images/117-2.png
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diff --git a/images/119-0.png b/images/119-0.png
new file mode 100644
index 0000000000000000000000000000000000000000..6f4bb9997a5789fadb7c38401b7ee03ab7169085
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